{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn import preprocessing\n",
    "from sklearn import model_selection\n",
    "import matplotlib.pyplot as plt \n",
    "from keras import layers\n",
    "from keras.models import Sequential\n",
    "from livelossplot.keras import PlotLossesCallback\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'MarmousiModel2.mat'#filename = 'MarmousiModel2_Changed.mat'\n",
    "marmousi_cube = sio.loadmat(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregar dados e cubos de Sísmica e Impedância"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "VP = marmousi_cube['Vp']\n",
    "#VS = marmousi_cube['Vs']\n",
    "#IS = marmousi_cube['IS']\n",
    "pimpedance = marmousi_cube['IP']\n",
    "seismic = marmousi_cube['seismic']\n",
    "\n",
    "WAVELET = marmousi_cube['wavelet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_traces = pimpedance.shape[1]\n",
    "\n",
    "train_wells_loc = np.arange(0,n_traces,80)\n",
    "all_wells_loc = np.arange(n_traces)\n",
    "\n",
    "wells_loc = np.array(list(set(all_wells_loc) - set(train_wells_loc)))\n",
    "valid_wells_loc, unlabed_wells_loc = model_selection.train_test_split(wells_loc,\n",
    "                                                                      test_size=0.9,\n",
    "                                                                      train_size=0.1,\n",
    "                                                                      shuffle=True)\n",
    "#valid_wells_loc = np.random.shuffle( np.arange(pimpedance.shape[1]))\n",
    "BATCH_SIZE = 10\n",
    "noise_dim = 2800#700\n",
    "IMG_SHAPE = (2800,1,1)#(700,1,1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizar Sismica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic_norm = seismic.flatten()\n",
    "ymin = 0\n",
    "ymax = 1\n",
    "seismic_norm = (ymax-ymin)*(seismic_norm-np.min(seismic_norm))/(np.max(seismic_norm)-np.min(seismic_norm)) + ymin\n",
    "seismic_norm = seismic_norm.reshape(seismic.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pimpedance_norm = pimpedance.flatten()\n",
    "ymin = 0\n",
    "ymax = 1\n",
    "pimpedance_norm = (ymax-ymin)*(pimpedance_norm-np.min(pimpedance_norm))/(np.max(pimpedance_norm)-np.min(pimpedance_norm)) + ymin\n",
    "pimpedance = pimpedance_norm.reshape(pimpedance.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizar estatísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"data_info = pd.DataFrame(zip(seismic_norm.flatten(),\\n                             pimpedance.flatten()),\\n                         columns=['Seismic CL Stats','PImp CL Stats'])\\ndata_info.describe()\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''data_info = pd.DataFrame(zip(seismic_norm.flatten(),\n",
    "                             pimpedance.flatten()),\n",
    "                         columns=['Seismic CL Stats','PImp CL Stats'])\n",
    "data_info.describe()'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparar os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.transpose(seismic_norm[:,train_wells_loc])\n",
    "Y_train = np.transpose(pimpedance[:-1,train_wells_loc])\n",
    "X_train = np.expand_dims(X_train,axis=(2,3))\n",
    "Y_train = np.expand_dims(Y_train,axis=(2,3))\n",
    "\n",
    "X_valid = np.transpose(seismic_norm[:,valid_wells_loc])\n",
    "Y_valid = np.transpose(pimpedance[:-1,valid_wells_loc])\n",
    "X_valid = np.expand_dims(X_valid,axis=(2,3))\n",
    "Y_valid = np.expand_dims(Y_valid,axis=(2,3))\n",
    "\n",
    "X_test = np.transpose(seismic_norm)\n",
    "Y_test = np.transpose(pimpedance[:-1,:])\n",
    "X_test = np.expand_dims(X_test,axis=(2,3))\n",
    "Y_test = np.expand_dims(Y_test,axis=(2,3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     IP (Y_train) shape:  (171, 2800, 1, 1)\n",
      "seismic (X_train) shape:  (171, 2800, 1, 1)\n",
      "     IP (Y_valid) shape:  (1343, 2800, 1, 1)\n",
      "seismic (X_valid) shape:  (1343, 2800, 1, 1)\n",
      "     IP (Y_test) shape:  (13601, 2800, 1, 1)\n",
      "seismic (X_test) shape:  (13601, 2800, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print('     IP (Y_train) shape: ',Y_train.shape)\n",
    "print('seismic (X_train) shape: ',X_train.shape)\n",
    "\n",
    "print('     IP (Y_valid) shape: ',Y_valid.shape)\n",
    "print('seismic (X_valid) shape: ',X_valid.shape)\n",
    "\n",
    "print('     IP (Y_test) shape: ',Y_test.shape)\n",
    "print('seismic (X_test) shape: ',X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizar Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Output P-Impedance Test - Ground True')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, axes1 = plt.subplots(nrows=3, ncols=2, figsize=(15,15))\n",
    "\n",
    "\n",
    "axes1[0,0].imshow(seismic)\n",
    "axes1[0,0].set_title(\"2D crossline seismic\")\n",
    "\n",
    "axes1[0,1].imshow(pimpedance)\n",
    "axes1[0,1].set_title(\"2D crossline P-Impedance\")\n",
    "\n",
    "\n",
    "axes1[1,0].imshow(X_train[:,:,0,0])\n",
    "axes1[1,0].set_title(\"Input Seismic Train\")\n",
    "\n",
    "axes1[1,1].imshow(Y_train[:,:,0,0])\n",
    "axes1[1,1].set_title(\"Output P-Impedance Train\")\n",
    "\n",
    "axes1[2,0].imshow(X_test[:,:,0,0])\n",
    "axes1[2,0].set_title(\"Input Seismic Test\")\n",
    "axes1[2,1].imshow(Y_test[:,:,0,0])\n",
    "axes1[2,1].set_title(\"Output P-Impedance Test - Ground True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação e Configuração da Rede"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the generator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ResBlock(inputRB):\n",
    "    RB0 = layers.Conv2D(filters=16,kernel_size=(300,1),padding='same')(inputRB)\n",
    "    RB = layers.BatchNormalization()(RB0)\n",
    "    RB = layers.ReLU()(RB)\n",
    "    RB = layers.Conv2D(filters=16,kernel_size=(3,1),padding='same')(RB)\n",
    "    RB = layers.BatchNormalization()(RB)\n",
    "    RB = layers.Add()([RB,inputRB])\n",
    "    RB = layers.ReLU()(RB)\n",
    "    return (RB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 2800, 1, 1)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 2800, 1, 1)   4           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "re_lu (ReLU)                    (None, 2800, 1, 1)   0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 2800, 1, 16)  4816        re_lu[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 2800, 1, 16)  64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_1 (ReLU)                  (None, 2800, 1, 16)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 2800, 1, 16)  784         re_lu_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 2800, 1, 16)  64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 2800, 1, 16)  0           batch_normalization_2[0][0]      \n",
      "                                                                 conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_2 (ReLU)                  (None, 2800, 1, 16)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 2800, 1, 16)  76816       re_lu_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 2800, 1, 16)  64          conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_3 (ReLU)                  (None, 2800, 1, 16)  0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 2800, 1, 16)  784         re_lu_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 2800, 1, 16)  64          conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 2800, 1, 16)  0           batch_normalization_4[0][0]      \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_4 (ReLU)                  (None, 2800, 1, 16)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 2800, 1, 16)  76816       re_lu_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2800, 1, 16)  64          conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_5 (ReLU)                  (None, 2800, 1, 16)  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 2800, 1, 16)  784         re_lu_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 2800, 1, 16)  64          conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 2800, 1, 16)  0           batch_normalization_6[0][0]      \n",
      "                                                                 conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_6 (ReLU)                  (None, 2800, 1, 16)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 2800, 1, 1)   49          re_lu_6[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 161,237\n",
      "Trainable params: 161,043\n",
      "Non-trainable params: 194\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def get_generator_model():\n",
    "    noise = layers.Input(shape=(noise_dim,1,1))\n",
    "\n",
    "    GEN = layers.Conv2D(filters=16,kernel_size=(300,1),padding='same')(noise)\n",
    "    GEN = layers.BatchNormalization()(noise)\n",
    "    GEN = layers.ReLU()(GEN)\n",
    "    \n",
    "    GEN = layers.Conv2D(filters=16,kernel_size=(300,1),padding='same')(GEN)\n",
    "    RB = layers.BatchNormalization()(GEN)\n",
    "    RB = layers.ReLU()(RB)\n",
    "    RB = layers.Conv2D(filters=16,kernel_size=(3,1),padding='same')(RB)\n",
    "    RB = layers.BatchNormalization()(RB)\n",
    "    RB = layers.Add()([RB,GEN])\n",
    "    GEN = layers.ReLU()(RB)\n",
    "    \n",
    "    GEN = layers.Conv2D(filters=16,kernel_size=(300,1),padding='same')(GEN)\n",
    "    RB = layers.BatchNormalization()(GEN)\n",
    "    RB = layers.ReLU()(RB)\n",
    "    RB = layers.Conv2D(filters=16,kernel_size=(3,1),padding='same')(RB)\n",
    "    RB = layers.BatchNormalization()(RB)\n",
    "    RB = layers.Add()([RB,GEN])\n",
    "    GEN = layers.ReLU()(RB)\n",
    "    \n",
    "    GEN = layers.Conv2D(filters=16,kernel_size=(300,1),padding='same')(GEN)\n",
    "    RB = layers.BatchNormalization()(GEN)\n",
    "    RB = layers.ReLU()(RB)\n",
    "    RB = layers.Conv2D(filters=16,kernel_size=(3,1),padding='same')(RB)\n",
    "    RB = layers.BatchNormalization()(RB)\n",
    "    RB = layers.Add()([RB,GEN])\n",
    "    GEN = layers.ReLU()(RB)\n",
    "        \n",
    "    GEN = layers.Conv2D(filters=1,kernel_size=(3,1),strides=(1,1),padding='same')(GEN)\n",
    "\n",
    "    GENmodel = keras.models.Model(inputs=noise,outputs=GEN)\n",
    "    return GENmodel\n",
    "\n",
    "f_model = get_generator_model()\n",
    "\n",
    "f_model.summary()\n",
    "#plot_model(f_model, show_shapes=True, show_layer_names=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#callback = tf.keras.callbacks.EarlyStopping(monitor='g_loss', patience=200)\n",
    "'''callback =  tf.keras.callbacks.EarlyStopping(monitor='loss', \n",
    "            mode='min',\n",
    "            restore_best_weights=True, \n",
    "            verbose=2, \n",
    "            patience=300)\n",
    "checkpoint = keras.callbacks.ModelCheckpoint(filepath='forward_model/autosaved_modeln_1d.hdf5', \n",
    "                             monitor='val_loss',\n",
    "                             verbose=1, \n",
    "                             save_best_only=True,\n",
    "                             mode='min')'''\n",
    "\n",
    "checkpoint = keras.callbacks.ModelCheckpoint(filepath='forward_model/autosaved_modeln_1d.hdf5',\n",
    "                                             monitor='val_mse',\n",
    "                                             verbose=1,\n",
    "                                             save_best_only=True,\n",
    "                                             mode='min')\n",
    "\n",
    "\n",
    "class PlotLosses(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.i = 0\n",
    "        self.x = []\n",
    "        self.losses = []\n",
    "        self.val_losses = []\n",
    "        \n",
    "        self.fig = plt.figure()\n",
    "        \n",
    "        self.logs = []\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        self.logs.append(logs)\n",
    "        self.x.append(self.i)\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.val_losses.append(logs.get('val_loss'))\n",
    "        self.i += 1\n",
    "        \n",
    "        clear_output(wait=True) \n",
    "        plt.plot(self.x[-100:], self.losses[-100:], label=\"loss\")\n",
    "        plt.plot(self.x[-100:], self.val_losses[-100:], label=\"val_loss\")\n",
    "        plt.legend()\n",
    "        plt.show();\n",
    "        \n",
    "plot_losses = PlotLosses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_model = get_generator_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate = 0.0005)\n",
    "#compile model using accuracy to measure model performance\n",
    "f_model.compile(optimizer=optimizer, loss='mse', metrics='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 01600: val_mse did not improve from 0.00001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ce05d0f4e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## train the model\n",
    "f_model.fit(Y_train, X_train,\n",
    "            validation_data=(Y_valid, X_valid),\n",
    "            epochs=1600,\n",
    "            verbose=1,\n",
    "            shuffle=True,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            callbacks = [plot_losses,checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f_model.load_weights('forward_model/autosaved_modeln.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d0458c4518>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict = f_model(Y_test[:5000,:,:])\n",
    "predict = np.transpose(predict[:,:,0,0].numpy())\n",
    "fig1, axes1 = plt.subplots(nrows=2, ncols=1, figsize=(15,15))\n",
    "axes1[0].imshow(predict)\n",
    "axes1[1].imshow(np.transpose(X_test[:,:,0,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "save = True\n",
    "if save:\n",
    "    # serialize model to JSON\n",
    "    model_json = f_model.to_json()\n",
    "    with open(\"forward_model/modelnorm_2d.json\", \"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "    # serialize weights to HDF5\n",
    "    f_model.save_weights(\"forward_model/modelnorm_2d.h5\")\n",
    "    print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0008081381592572838"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_trace = np.random.randint(predict.shape[1])\n",
    "plt.plot(predict[:,test_trace])\n",
    "plt.plot(X_test[test_trace,:,0]+0)\n",
    "mse = np.mean((X_test[test_trace,:,0]-predict[:,test_trace])**2)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''models = []\n",
    "errors = []\n",
    "for i in range(20):\n",
    "    print('- '*7)\n",
    "    f_model = get_generator_model()\n",
    "    f_model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n",
    "    f_model.fit(Y_train, X_train,\n",
    "            validation_data=(Y_valid, X_valid),\n",
    "            epochs=2000,\n",
    "            verbose=0,\n",
    "           batch_size=5,\n",
    "                \n",
    "           callbacks = tf.keras.callbacks.EarlyStopping(monitor='loss', \n",
    "            mode='min',\n",
    "            restore_best_weights=True, \n",
    "            verbose=2, \n",
    "            patience=500)\n",
    "                \n",
    "               )\n",
    "    error_mse = np.mean(((Y_test-f_model(X_test))**2)[:])\n",
    "    models.append(f_model)\n",
    "    errors.append(error_mse)\n",
    "    print('training done, i: {}, loss: {}'.format(i,error_mse))'''\n",
    "''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''min_value = min(errors)\n",
    "min_index = errors.index(min_value)\n",
    "print('best: ',min_index)'''\n",
    "''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''f_model = models[min_index]'''\n",
    "''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
